Table of Contents

Class BGE<T>

Namespace
AiDotNet.NeuralNetworks
Assembly
AiDotNet.dll

BGE (BAAI General Embedding) neural network implementation. A state-of-the-art retrieval model known for its high accuracy across diverse benchmarks.

public class BGE<T> : TransformerEmbeddingNetwork<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable, IEmbeddingModel<T>

Type Parameters

T

The numeric type used for calculations (typically float or double).

Inheritance
BGE<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

BGE is a series of open-source embedding models from the Beijing Academy of Artificial Intelligence (BAAI). These models are specifically optimized for retrieval tasks using a multi-stage training curriculum that includes massive-scale pre-training and fine-grained instruction tuning.

For Beginners: BGE is currently one of the "smartest" search engines in the world. It has been trained like a student who went through elementary school (general reading), high school (specific facts), and then a PhD program (answering hard questions). This makes it incredibly good at understanding exactly what you're looking for, even if your query is phrased in a confusing way.

Constructors

BGE(NeuralNetworkArchitecture<T>, ITokenizer?, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, int, int, int, int, int, int, PoolingStrategy, ILossFunction<T>?, double)

Initializes a new instance of the BGE model.

public BGE(NeuralNetworkArchitecture<T> architecture, ITokenizer? tokenizer = null, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, int vocabSize = 30522, int embeddingDimension = 768, int maxSequenceLength = 512, int numLayers = 12, int numHeads = 12, int feedForwardDim = 3072, TransformerEmbeddingNetwork<T>.PoolingStrategy poolingStrategy = PoolingStrategy.ClsToken, ILossFunction<T>? lossFunction = null, double maxGradNorm = 1)

Parameters

architecture NeuralNetworkArchitecture<T>

The configuration defining the model structure.

tokenizer ITokenizer

Optional tokenizer for text processing.

optimizer IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>

Optional optimizer for training.

vocabSize int

The size of the vocabulary (default: 30522).

embeddingDimension int

The dimension of the embeddings (default: 768).

maxSequenceLength int

The maximum length of input sequences (default: 512).

numLayers int

The number of transformer layers (default: 12).

numHeads int

The number of attention heads (default: 12).

feedForwardDim int

The hidden dimension of feed-forward networks (default: 3072).

poolingStrategy TransformerEmbeddingNetwork<T>.PoolingStrategy

The strategy used to aggregate token outputs (default: ClsToken).

lossFunction ILossFunction<T>

Optional loss function.

maxGradNorm double

Maximum gradient norm for stability (default: 1.0).

Methods

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

Embed(string)

Encodes a single string into a normalized summary vector.

public override Vector<T> Embed(string text)

Parameters

text string

The text to encode.

Returns

Vector<T>

A normalized embedding vector.

Remarks

For Beginners: This is the main use case. You give the model a sentence, it reads it with all its layers, summarizes the meaning based on your chosen pooling strategy (like taking the average meaning), and returns one final list of numbers.

EmbedAsync(string)

Asynchronously embeds a single text string into a vector representation.

public override Task<Vector<T>> EmbedAsync(string text)

Parameters

text string

The text to embed.

Returns

Task<Vector<T>>

A task representing the async operation, with the resulting vector.

EmbedBatchAsync(IEnumerable<string>)

Asynchronously embeds multiple text strings into vector representations in a single batch operation.

public override Task<Matrix<T>> EmbedBatchAsync(IEnumerable<string> texts)

Parameters

texts IEnumerable<string>

The collection of texts to embed.

Returns

Task<Matrix<T>>

A task representing the async operation, with the resulting matrix.

GetModelMetadata()

Retrieves metadata about the BGE model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

Metadata containing model type and naming information.

InitializeLayers()

Configures the transformer layers for the BGE model using optimized retrieval defaults from LayerHelper.

protected override void InitializeLayers()

Remarks

For Beginners: This method builds the model's "library index." It sets up a powerful transformer brain and a final precision checkpoint (layer normalization) that makes sure every coordinate it creates is perfect for high-speed searching.

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

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